This invention relates to an apparatus and method for detecting fires by analysis of images of potential flames.
Fires emit a range of wavelengths. The art of optical fire detection is based upon sensing types of light that are characteristic of fires. More sophisticated detectors also analyze the light to exclude possible false alarms.
It is well known to use one or several individual sensors in a fire detector. Typically the sensors are sensitive to particular infrared and/or ultraviolet wavelength bands of light that are known to be present in most fires.
A significant disadvantage of such detectors is that they are subject to false alarms, as many non-flame sources also produce infrared and ultraviolet light in the same wavelength bands. Common false alarm sources include but are not limited to artificial lighting, sunlight, and arc welding. One source of false alarms that is particularly troublesome is that of reflections. Reflections from water, metal, etc. can in many ways mimic actual fires. This is especially true when the source of the reflection is an actual fire. There are many circumstances, for example petroleum drilling and refining, wherein known actual fires are present proximate the detector but outside the area being monitored.
More recently, it has become possible to use electronic cameras to produce images which are then analyzed to identify potential fires, a process called “flame imaging”. Flame imaging allows for precise detection of the location of flames within the area protected, since the location of flames within the image may be clearly identified. In addition, electronic cameras produce images with a large number of picture elements (or pixels), typically at least several thousand and up to at least several million. It will be appreciated that this large number of pixels can provide data regarding flames that simply cannot be obtained from a fire detector having only one or at most a few sensors. However, as with individual sensors, flame image analysis is often subject to false alarms.
Indeed, known flame imaging systems often may be more susceptible to false alarms than individual sensors. A wide variety of image artifacts may trigger false alarms by virtue of their brightness, color, shape, motion, etc. Because of this, flame imaging systems are often relied upon to confirm fires identified by conventional flame detectors, rather than to detect fires independently.
A further problem with conventional flame image systems is that the image settings appropriate for flame imaging are not appropriate viewing non-flame images. This is especially true indoors, at night, or in other poorly lit environs. Because flames are extremely bright, image settings (exposure time, iris, etc.) must be selected so as to properly expose the flame. In this way, the images of the bright flames show sufficient detail for analysis. However, at such image settings the remaining (non-flame) portion of the image can be so dark that almost nothing can be seen in it. In particular, objects and persons that may be distant from the flame cannot normally be identified, either by humans or by data processing routines. As a result, an image optimal for flame detection is not optimally suited for other purposes, in particular human viewing, because practically nothing but the flames can be distinguished.
Conversely, if the image settings are such that objects and persons can be identified, the image is “overexposed” so that flames generally appear as shapeless, poorly defined bright spots. These images reveal little or no structure or color within the flame itself, thus limiting meaningful analysis. Indeed, at such settings it can be difficult even to determine whether a bright spot is a fire at all, or whether it is some other bright phenomenon such as reflected sunlight or an incandescent bulb.
For this reason, flame imaging systems conventionally require dedicated cameras, useful for no other purpose.
Conventional methods for processing the data obtained from flame imaging cameras also have disadvantages. Typically, known flame imaging systems process image data in one of two ways. First, the data present in a single image may be analyzed on its own. This has the advantage of minimizing the number of calculations necessary, since the data is limited to what is present in a single image. However, analysis of a single image does not yield any information related to changes in the image over time. Flames change in shape, size, position, etc. over the course of time, and analysis of these changes can be useful both for detecting flames and for excluding false alarms. Such analysis is not possible with only a single image.